We consider the problem of partitioning of an electronic system into k partitioning blocks taking into account the minimisation of the number of off-chip wires, the area of substrate and the power dissipated in blocks...
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To overcome the shortcomings of Multi-Objectives evolutionary algorithms (MOEAs) based on the notion of Objective-Space-Dividing (OSD) with high calculation complexity, this paper proposes an improved algorithm called...
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How to evaluate the fitness of individual and how to generate the offspring of parents are two issues in TSP evolutionary algorithm. In insertion operator, the parents and its offspring have the same segment, which do...
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We introduce Embodied Evolution (EE) as a methodology for the automatic design of robotic controllers. EE is an evolutionary robotics (ER) technique that avoids the pitfalls of the simulate-and-transfer method, allows...
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The paper depicts an design approach for the proposition of a new propulsion unit for a light electric vehicle (EV). For the given main data of the application, four motor variants are studied, numerically evaluated b...
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A New Linear Optimization technique coupling evolutionary algorithm for Solving Multiobjective Optimization Problems (NLEA) based on real-coded method is proposed after analyzing the drawbacks of existing evolutionary...
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ISBN:
(纸本)9780769537368
A New Linear Optimization technique coupling evolutionary algorithm for Solving Multiobjective Optimization Problems (NLEA) based on real-coded method is proposed after analyzing the drawbacks of existing evolutionary algorithms in this paper. One of the main advantages of the proposed approach is that search space of constrained dominance problems with high dimensions is compressed into two dimensions. NLEA has a linear fitness function in two dimension space so as to evaluate fitness of each individual fast in population. A crossover operator based on density function and a new mutation operator is developed to extend the search space and extract the better solution. In our tests, A few benchmark multi-objective optimization problems which divided into two groups are taken to test this algorithm. The numerical experiments show that proposed approach is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions. Multiobjective optimization problems, evolutionary algorithms, Pareto optimal solutions, Linear function.
The optimal solution to minimizing the maximum tardiness in single machine scheduling is obtained by the Earliest Due Date (EDD) rule if ready times are zero for all jobs. In the case of non-zero ready times, preempti...
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The optimal solution to minimizing the maximum tardiness in single machine scheduling is obtained by the Earliest Due Date (EDD) rule if ready times are zero for all jobs. In the case of non-zero ready times, preemption becomes a significant consideration in providing a solution. Preemption allowed version is solved optimally by using the Modified Earliest Due Date (MEDD) procedure. However, the version of preemption not allowed is known as NP-hard and delay and non-delay strategies might be used in a hybrid fashion. This paper focuses on minimizing the maximum tardiness in the presence of non-zero times and when preemption is not allowed. The proposed method is evolutionary programming (EP). The results indicate that EP produces optimal / near optimal results consistently.
In this paper, a generalization of the original Quantum-Inspired evolutionary Algorithm (QIEA): the Generalized Quantum-Inspired evolutionary Algorithm (GQIEA) is proposed. Like QIEA, GQIEA is also based on the quantu...
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In this article we propose a novel distance domination parameter and describe a multiobjective evolutionary concept called distance domination based multiobjective evolutionary algorithm (DBMEA). The distance paramete...
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We introduce a novel evolutionary algorithm where the centralized oracle -the selection-reproduction loop- is replaced by a distributed system of Fate Agents that autonomously perform the evolutionary operations. This...
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